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dc.contributor.editorClevert, Djork-Arné
dc.contributor.editorWand, Michael
dc.contributor.editorMalinovská, Kristína
dc.contributor.editorSchmidhuber, Jürgen
dc.contributor.editorTetko, Igor V.
dc.date.accessioned2024-10-21T15:27:03Z
dc.date.available2024-10-21T15:27:03Z
dc.date.issued2025
dc.identifierONIX_20241021_9783031723810_28
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/93866
dc.description.abstractThis open Access book constitutes the refereed proceedings of the First International Workshop on AI in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. The 12 papers presented here were carefully reviewed and selected for these open access proceedings. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
dc.languageEnglish
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PN Chemistry::PNR Physical chemistry::PNRA Computational chemistry
dc.subject.otherSynthesis planning
dc.subject.otherchemo-informatics
dc.subject.otherbig data
dc.subject.otherdeep learning
dc.subject.otherdrug discovery
dc.subject.otherconvolution neural networks toxicity
dc.subject.otherGNNs
dc.subject.othertransformers
dc.subject.otherexplainable AI
dc.subject.otheractive learning
dc.subject.otherfeature decomposition
dc.subject.otherde novo molecular design
dc.subject.otherquantum-mechanical properties
dc.subject.othersolvent effects
dc.subject.othermolecular property prediction
dc.subject.otherconvergent routes
dc.subject.otherequivariant graph neural networks
dc.subject.otherstructure-based drug discovery
dc.subject.otherconstraints
dc.titleAI in Drug Discovery
dc.title.alternativeFirst International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Lugano, Switzerland, September 19, 2024, Proceedings
dc.typebook
oapen.identifier.doi10.1007/978-3-031-72381-0
oapen.relation.isPublishedBy6c6992af-b843-4f46-859c-f6e9998e40d5
oapen.relation.isFundedBy00d1f756-909d-4cbf-8eb4-51cca261bca3
oapen.relation.isbn9783031723810
oapen.relation.isbn9783031723803
oapen.imprintSpringer Nature Switzerland
oapen.series.number14894
oapen.pages176
oapen.place.publicationCham
oapen.grant.number[...]


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